Data Analysis¶
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#导入模块
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import seaborn as sn
%matplotlib inline
#创建特征列表表头
#column_names = ['Sample code number','Clump Thickness','Uniformity of Cell Size','Uniformity of Cell Shape','Marginal Adhesion','Single Epithelial Cell Size','Bare Nuclei','Bland Chromatin','Normal Nucleoli','Mitoses','Class']
column_names = ['id','diagnosis','radius_mean','texture_mean','perimeter_mean','area_mean','smoothness_mean','compactness_mean','concavity_mean','concave points_mean','symmetry_mean','fractal_dimension_mean','radius_se','texture_se','perimeter_se','area_se','smoothness_se','compactness_se','concavity_se','concave points_se','symmetry_se','fractal_dimension_se','radius_worst','texture_worst','perimeter_worst','area_worst','smoothness_worst','compactness_worst','concavity_worst','concave points_worst','symmetry_worst','fractal_dimension_worst']
#使用pandas.read_csv函数从网上读取数据集
#data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data',names=column_names)
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data',names=column_names)
df.info()
属性信息:¶
- 1）身份证号码
2）诊断（M =恶性，B =良性）
3-32为每个细胞核计算十个实值特征：
- a）半径（从中心到周边点的距离的平均值）
- b）纹理（灰度值的标准偏差）
- c）周界
- d）区域
- e）光滑度（半径长度的局部变化）
- f）紧凑性（周长^ 2 /面积 - 1.0）
- g）凹度（轮廓凹部的严重程度）
- h）凹点（轮廓的凹入部分的数量）
- i）对称
- j）分形维数（“海岸线近似” - 1）
- 对每个数据分别求平均值，标准误差，“最差”或最大，产生30个特征。
- 所有功能值都用四位有效数字重新编码。
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df.head(10)
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sn.countplot(df["diagnosis"],label='count')
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# id 不用于classification，删去，diagnosis为分类标准
## feature的名字并不需要了解 because I believe machine learning is awesome :)
# df['diagnosis'] = pd.get_dummies(df['diagnosis'],drop_first=True)
dia = df['diagnosis']
df.drop('id', axis=1,inplace=True)
df.drop('diagnosis',axis=1,inplace=True)
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data = df
data.describe()
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Data Visualization¶
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#构建散布矩阵（scatter matrix)
#通过散布矩阵可以看出在这个数据特征和其它特征中有关联性
pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (80,60), diagonal = 'kde');
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#相似相关性热力图
plt.subplots(figsize=(20, 20))
sn.heatmap(data.corr(), annot=True, linewidths=.5, fmt= '.2f', cmap='coolwarm')
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